Related papers: Host-Based Network Intrusion Detection via Feature…

The present research investigates how to improve Network Intrusion Detection Systems (NIDS) by combining Machine Learning (ML) and Deep Learning (DL) techniques, addressing the growing challenge of cybersecurity threats. A thorough process…

Cyberattacks are a major issues and it causes organizations great financial, and reputation harm. However, due to various factors, the current network intrusion detection systems (NIDS) seem to be insufficent. Predominant NIDS identifies…

With the increased use of network technologies like Internet of Things (IoT) in many real-world applications, new types of cyberattacks have been emerging. To safeguard critical infrastructures from these emerging threats, it is crucial to…

State-of-the-art deep learning (DL)-based network intrusion detection systems (NIDSs) offer limited "explainability". For example, how do they make their decisions? Do they suffer from hidden correlations? Prior works have applied…

Intrusion Detection Systems (IDSs) have played a significant role in the detection and prevention of cyber-attacks in traditional computing systems. It is not surprising that this technology is now being applied to secure Internet of Things…

Machine learning (ML), especially deep learning (DL) techniques have been increasingly used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has shown to be extremely vulnerable to adversarial attacks, especially…

Timely response of Network Intrusion Detection Systems (NIDS) is constrained by the flow generation process which requires accumulation of network packets. This paper introduces Multivariate Time Series (MTS) early detection into NIDS to…

The rapid growth of the Internet of Things (IoT) has revolutionized industries, enabling unprecedented connectivity and functionality. However, this expansion also increases vulnerabilities, exposing IoT networks to increasingly…

Network security has become the biggest concern in the area of cyber security because of the exponential growth in computer networks and applications. Intrusion detection plays an important role in the security of information systems or…

Network Intrusion Detection (NID) remains a key area of research within the information security community, while also being relevant to Machine Learning (ML) practitioners. The latter generally aim to detect attacks using network features,…

Network Intrusion Detection System (NIDS) is a key component in securing the computer network from various cyber security threats and network attacks. However, consider an unfortunate situation where the NIDS is itself attacked and…

With massive data being generated daily and the ever-increasing interconnectivity of the world's Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and…

Intrusion detection system (IDS) is a piece of hardware or software that looks for malicious activity or policy violations in a network. It looks for malicious activity or security flaws on a network or system. IDS protects hosts or…

Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where benign and malicious samples are clearly labelled. Such labels…

Cybersecurity is one of the foremost challenges facing the world of cloud computing. Recently, the widespread adoption of smart devices in cloud computing environments that provide Internet-based services has become prevalent. Therefore, it…

In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the…

An Intrusion Detection System (IDS) is vital in cybersecurity, detecting unauthorized activity across networks. With attacks on network layers increasing, stronger IDSs are needed. Yet most IDSs rely on centralized detection, forcing IoT…

In intrusion detection systems, classifiers still suffer from several drawbacks such as data dimensionality and dominance, different network feature types, and data impact on the classification. In this paper two significant enhancements…

With the increasing number of new attacks on ever growing network traffic, it is becoming challenging to alert immediately any malicious activities to avoid loss of sensitive data and money. This is making intrusion detection as one of the…

As network security threats evolve, safeguarding flow-based Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS) from evasion adversarial attacks is crucial. This paper introduces the notion of feature perturb-ability and…